The candidate in this Mentored Quantitative Research Career Development Award (K25) will be supported in training and research as she makes the transition from mathematics to microbiology. This candidate will combine rigorous training in the field of microbiology with her quantitative and mathematical skills in order to gai an in-depth understanding of bacterial virulence factors associated with influenza infection. The training and research will be performed in the Department of Infectious Diseases at St. Jude Children's Research Hospital under the mentorship of Dr. Jonathan McCullers (experimental microbiology) and co-mentorships of Dr. Frederick Adler (theoretical population biology, University of Utah) and Dr. Alan Perelson (theoretical virology and immunology, Los Alamos National Laboratory). The candidate's long-term goal is to establish research independence at the interface of experimental and theoretical microbiology. The objective and goal of this research plan is to determine the relative contributions of Streptococcus pneumoniae genes to pathogenesis of influenza infections through experimental and theoretical methods and tools. The K25 award will support a research and training program that includes intensive coursework, attendance at conferences, meetings and seminars, hands-on training in experimental microbiology, and a research plan that provides detailed quantitative studies to understand the host-pathogen interactions during bacterial infections in influenza-infected hosts. The proposed framework integrates targeted experimental studies, inference of population genetics, and rigorous mathematical modeling. The specific aims of this proposal are to: (1) perform experimental in vivo studies of influenza-S. pneumoniae infections in mice, (2) develop/refine mathematical models and computational simulations of the kinetics of viral-bacterial interactions, and (3) analyze data, estimate parameters and test specific hypotheses with regard to the influenza-S. pneumoniae dynamics. In the experimental studies, we will measure the fitness, frequency, and pathogenicity of bacterial mutants produced within the contexts of naive infections and influenza infections. Using the mathematical models and simulation, the data will be analyzed in order to obtain quantitative information about the kinetic differences of each individual bacterial mutant and of the entire population of mutants. The iterative combination of experiments, mathematical models, and computational simulations will result in a detailed and quantitative understanding of the viral-bacterial co-infection dynamics. Such an approach is of critical importance to understand the complex interplay of viral-bacterial interactions and for identification of novel targets for vaccine and antimicrobial development expected to be important to combat these pathogens.